[Thread(pool)] Memory issue

Dear all,

I have a memory consumption question. I am tasks in a pooled thread scenario. My problem is that the memory consumption is very, very high. The final result is typically >40MB but the memory consumption is >> 60GB.

I am wondering because the threads should free their memory once they are finished. But it seems as they do not do it.

The actual rouine shall filter a data set. Parts are generated and freed within the thread. The filtered data is the send to a channel.

I checked it with heaptrack and could see a linear growth in memory. Which is ok, but it is far too steep.

I was expecting that the threads free the used memory once they are finished, but it seems not to be the case. What is wrong here?

This is the code:

    // set is the input and a Vec<Vec<LargeDataStructure>>

    // The basic Routine 
    let tuple_gen = Arc::new(Mutex::new(TupleGenerator::new(&set)));

    // arc the set
    let set = Arc::new(set);

    // I divide the `set` into chunks and calculate each chunk in a thread
    let batch_size = calc_batch_size(&final_size, &num_cpus::get(), nb_of_sets);

    let result: Arc<Mutex<Vec<Vec<LargeDataStructure>>>> = Arc::new(Mutex::new(vec![]));

    let pool = ThreadPool::new(num_cpus::get() as usize - 1);

    let (tx, rx) = channel();

    for bn in 0..batch_nb + 1 {

        let intermediate_ = intermediate.clone();
        let set_ = set.clone();
        let tuple_gen_ = tuple_gen.clone();
        let tx_ = tx.clone();

        pool.execute(move || {
            let tuples = tuple_gen_.lock().unwrap().get_next_batch();

            let mut tmp_result: Vec<Vec<SumComposition>> = vec![];
            // this for-loop consumes most of the memory
            for tuple in tuples {

                // generate a sumcomposition tuple from the index tuple
                let mut tmp = vec![];
                for (j, index) in tuple.iter().enumerate() {

                // if CONSTRAINTS are given, check it
                if intermediate_.has_suchthat {
                    let left = get_left_side(&tmp, &intermediate_.terms); // -> LargeDataStructure
                    let right = get_right_side(&tmp, &intermediate_.terms); // -> LargeDataStructure
                    if left == right {
                } else {
            // everything should be freed here

            // the result is send to the parent thread

    for nb in 0..batch_nb + 1 {
        let tmp_result = rx.recv().unwrap();


Why are you cloning the set? I am assuming this is the problem, especially given you only need read access to this data. Trying creating let set_ = &set right after set definition, and using that from the threads.

Sorry, this was a typo. I wanted to trim the code for this question. The set is wrapped with Arc<>. Thus, the set.clone() should just clone the reference.

Check with a heap profiler. On Linux there's valgrind + massif. On macOS there's excellent Instruments.app with an Allocations profiler. When you build with debug symbols, they should show you what takes so much space and a line number where it came from.

I did. The result was that the functions get_left_side() and get_right_side() cause the most memory usage.

I thought, however, that once a thread is finished, it frees this memory. But it seems not doing it. And I really wonder why??

There are thousands of threads to be processed in this thread pool and after some time (around half an hour) the memory consumption is so high that it crashes. But if the memory of the threads would be freed, it would be no problem even with millions of threads. What am I missing here? I am sitting on this problem for too long ...

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